which can be used to update the DQN parameters using stochastic gradient descent.
In the context of Deep Reinforcement Learning [1]} two networks corresponding to the two Q functions are used (as in [2]}) and Eq. (REF ) becomes:
\(\begin{split}L(\theta _t) &= ({blue}{r}_{t+1} + \gamma Q(s_{t+1}, {argmax}_{a^{^{\prime }}}Q(s_{t+1},{blue}{ a^{^{\prime }};\theta _t});\theta _t^{-}) \\&- Q(s_t, a_t; \theta _t))^2\end{split}\) 
